The primary mission of the Core is to help NIH researchers with analyses of their functional MRI (brain activation mapping) data. Several levels of help are provided, from short-term immediate aid to long-term development and planning. Consultations: The shortest term help comprises consultations with investigators about issues that arise in their research. About 180 such in-person consultations were logged in FY 2009 (including each of the NIH groups listed as NIH Collaborators), and 3600+ messages recorded on our Web forum (about 50% from non-NIH sources). The issues are quite varied, since there are many steps in carrying out fMRI data analyses. Common problems include: - How to set up experimental design so that data can be analyzed effectively? - Interpretation and correction of MRI imaging artifacts (a common one comes from subject head motion during scanning). - How to set up the time series analysis to extract brain activation effects of interest, and to suppress non-activation artifacts? - How to analyze data to reveal connections between brain regions during certain mental tasks, or at rest? - How to carry out inter-patient (group) statistical analysis, especially when non-MRI data (e.g., genetic information, age, disease status) needs to be incorporated? There are familiar themes in many of these consultations, but each meeting and each experiment raises unique questions and usually requires delving into the goals and details of the research project in order to ensure that nothing crucial is being missed. Complex statistical issues are often raised. Sometimes new software needs to be developed to help researchers answer their specific questions. Educational Efforts: The Core has developed a 40 hour course on how to design and analyze fMRI data. This course is taught in a one week hands-on "bootcamp", and was taught twice during FY 2009 (Feb and Sep). All material for this continually evolving course (sample data, scripts, and PowerPoint/PDF slides) are freely available on our Web site http://afni.nimh.nih.gov . The course material includes several sample datasets that are used to illustrate the entire process, starting with images output by MRI scanners and continuing through to the collective statistical analysis of groups of subjects. We also taught this course at 3 non-NIH sites this year: Princeton University (Jan), Dartmouth College (Mar), and UC San Diego (Aug). Two presentations were given at a brain imaging workshop at McGill University (Aug), and one at a scientific conference (Apr). Two full days of advanced fMRI technique workshops were also given at the NIH in 2009 (May and Jun). Algorithm and Software Development: The longest term support consists of developing new methods and software for fMRI data analysis, both to solve immediate problems and in anticipation of new needs. All of our software is incorporated into the AFNI package, which is Unix/Linux/Macintosh-based open-source and is available for download by anyone. New programs are created, and old programs modified, in response to specific user requests and in response to the Core's vision of what will be needed in the future. AFNI is "pushed" to NIH computers whenever updates are made;users on non-NIH systems must download the software. Notable developments during FY 2009 include: - Extensive updating of our new method for correcting the statistical analysis of functional brain time series data for temporal correlation in the data's noise. - Creation and implementation of a new method for combining multiple subjects'brain activation data into group activation maps. This new technique builds on the "meta-analysis" methods developed in the 1990s for combining the results of multiple clinical studies, but applies these complex tools directly to each pixel in the brain maps. The first users have been very happy to get cleaner and more powerful activation maps, essentially "for free" (no new data needs to be gathered -- "old" data can be re-analyzed with the combination of the new methods). - In FY2008, we developed a new method for aligning brain activation data to anatomically correct brain images. This method is somewhat tricky to use correctly, so we developed a user interface to make most of the decisions for the user. - Two nonlinear fitting methods were developed for use with specialized MRI data where contrast agent drugs are given to the subject and then the altered MRI data is followed through time to reveal something about the subject's physiology. The first case is for fitting Gd-DTPA infiltration into brain tumors;the second case is for fitting manganese contrast infiltration into the olfactory bulb in non-human primate studies. - Several new tools were developed for analysis of resting state (non-task oriented) functional brain time series data. InstaCorr is an interactive tool for exploring the functional connectivity of one subject's data. GC is a non-interactive tool for calculating the "Granger causality" connections between specified brain regions. - A new tool to automate task-based fMRI data analyses has been developed (and is still evolving). The goal is to make it easy to carry out standard brain mapping statistics. We are now recommending that all AFNI users use this tool to carry out their work, if practicable. - Many small-to-medium changes were made to the software in response to specific NIH researcher requests and needs. Many small bug fixes were made -- we pride ourselves on fixing bugs in AFNI rapidly. Our development plans include the incorporation of more brain atlases into the AFNI software. The human atlases already present have been very helpful to users, and there have been many requests for rodent and primate atlases to be added. Another project underway is the development of a novel method for segmenting brain image datasets into gray matter, white matter, and cerebrospinal fluid regions. Current methods rely on brain atlases derived from multiple subjects, which bias the results towards the mean segmentation -- in one notorious case, the image of a bottle of water was "successfully" segmented by one atlas-based software tool, with no indication of error. Another project is to extend the InstaCorr method for visually exploring individual subject brain connectivity maps to work with groups of subjects. Extramural Collaborations: - We helped update Dr Lawrence Frank's (UCSD) diffusion imaging software in AFNI; - We incorporated into AFNI more brain atlas databases developed by Dr Karl Zilles (Julich); - We updated Dr Stephen Laconte's (Baylor) brain-state classifier software to be usable in realtime;Dr Laconte came to the NIH to teach a class on his software. - We worked with Dr Michael Beauchamp (UT Houston) to improve our image registration software. Public Health Impact: Thus far in FY 2009 (Oct-Aug), the principal AFNI publication has been cited in 250 papers: 38 from the NIH, and the rest from extramural institutions. Most of our work supports basic research into brain function, but some of our work is more closely tied to or applicable to specific diseases: - We collaborate with Dr Alex Martin (NIMH) to apply our resting state analysis methods to autism spectrum disorder. - Our task-based fMRI analysis tools are widely used in the NIMH's Mood and Anxieties Disorders program. - Our Gd-DTPA nonlinear analysis method is used in the NIH Clinical Center to analyze data from brain cancer patients. At this time, our tool is the only freely available software for such data analyses. - Our precise registration tools are important for individual subject applications of brain mapping, such as pre-surgical fMRI planning. - Our realtime fMRI software is being used for studies on brain mapping feedback in neurological disorders, and is also used for quality control at the NIH fMRI scanners.